Zobrazeno 1 - 10
of 1 390
pro vyhledávání: '"Gómez Alberto"'
Autor:
Yuan, Zhen, Stojanovski, David, Li, Lei, Gomez, Alberto, Jogeesvaran, Haran, Puyol-Antón, Esther, Inusa, Baba, King, Andrew P.
Splenomegaly, the enlargement of the spleen, is an important clinical indicator for various associated medical conditions, such as sickle cell disease (SCD). Spleen length measured from 2D ultrasound is the most widely used metric for characterising
Externí odkaz:
http://arxiv.org/abs/2411.11190
We investigate the utility of diffusion generative models to efficiently synthesise datasets that effectively train deep learning models for image analysis. Specifically, we propose novel $\Gamma$-distribution Latent Denoising Diffusion Models (LDMs)
Externí odkaz:
http://arxiv.org/abs/2409.19371
Autor:
Bransby, Kit M., Kim, Woo-jin Cho, Oliveira, Jorge, Thorley, Alex, Beqiri, Arian, Gomez, Alberto, Chartsias, Agisilaos
Building an echocardiography view classifier that maintains performance in real-life cases requires diverse multi-site data, and frequent updates with newly available data to mitigate model drift. Simply fine-tuning on new datasets results in "catast
Externí odkaz:
http://arxiv.org/abs/2407.21577
Autor:
Bransby, Kit Mills, Beqiri, Arian, Kim, Woo-Jin Cho, Oliveira, Jorge, Chartsias, Agisilaos, Gomez, Alberto
Neural networks can learn spurious correlations that lead to the correct prediction in a validation set, but generalise poorly because the predictions are right for the wrong reason. This undesired learning of naive shortcuts (Clever Hans effect) can
Externí odkaz:
http://arxiv.org/abs/2406.19148
Autor:
Reynaud, Hadrien, Meng, Qingjie, Dombrowski, Mischa, Ghosh, Arijit, Day, Thomas, Gomez, Alberto, Leeson, Paul, Kainz, Bernhard
To make medical datasets accessible without sharing sensitive patient information, we introduce a novel end-to-end approach for generative de-identification of dynamic medical imaging data. Until now, generative methods have faced constraints in term
Externí odkaz:
http://arxiv.org/abs/2406.00808
Autor:
Miguel-Gómez, Alberto
We provide a model-theoretic classification of the countable homogeneous $\mathbf{H}_4$-free 3-hypertournament studied by Cherlin, Hubi\v{c}ka, Kone\v{c}n\'y, and Ne\v{s}et\v{r}il. Our main result is that the theory of this structure is $\mathrm{SOP}
Externí odkaz:
http://arxiv.org/abs/2404.04381
U-Net style networks are commonly utilized in unsupervised image registration to predict dense displacement fields, which for high-resolution volumetric image data is a resource-intensive and time-consuming task. To tackle this challenge, we first pr
Externí odkaz:
http://arxiv.org/abs/2307.02997